摘要
以技术创新平台为背景,针对原有协同过滤算法推荐滞后以及算法可扩展性差的问题,根据用户的实时反馈,在Slope One算法的基础上,提出了更新增量机制,分解出固定因子以及增量因子,当用户对项目的评分改变时,只需更新增量因子,提高了算法的可扩展性,更精确地反应了用户的兴趣变化。经算例验证,该算法在保证推荐精度的同时可以有效地缩短推荐时间。
Set in technology innovation platform , traditional collaborative filtering algorithm which has the prob-lem of lagged recommendation and bad scalability is focused on .According to users ’ real-time feedback, incremental update mechanism is introduced , which detaches fixed factor and incremental factor .When the score is changed , only incremental factor needs to be changed , which improves the scalability of the algorithm and reflects the users ’ interest change more accurately .The experimental results indicate that the algorithm ensures accuracy of the recommendation and at the same time reduces the recommendation time .
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2015年第1期89-92,共4页
Operations Research and Management Science
基金
中国博士后基金(2013M531259)
教育部人文社科青年基金项目(13YJC630116)
江苏省博士后基金(1202103C)
江苏省教育厅科技项目(2012JSSPITP0904)
南京市鼓楼区科技计划项目(2013018)
关键词
管理科学与工程
增量更新机制
SLOPE
ONE
算法
用户行为
实时推荐
management science and engineering
incremental updating mechanism
slope one algorithm
user behavior
real-time recommendation
